Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues for Human Participatory Based on...

Area of Science:

• Computer Science
• Information Security
• Data Mining

Background:

• Global cyberspace security is a complex issue with interconnected challenges.
• Public perception often stereotypes cyberspace problems, overlooking their intricate relationships.

Purpose of the Study:

• To analyze global cyberspace security issues and their associations.
• To challenge common perceptions by revealing interdependencies in cyberspace.
• To identify key areas within global cybersecurity.

Main Methods:

• Utilized the Apriori algorithm for association rule mining.
• Analyzed 56,096 web pages across 40 target websites.
• Evaluated metrics including support, confidence, leverage, and reliability.

Main Results:

• Discovered 181 strong association rules related to global cyberspace security.
• Found that 69.6% of professional websites discussed cybersecurity, versus only 2% of non-professional sites.
• Identified core issues: internet sovereignty, cyber attack, cyber crime, data leakage, and data protection.

Conclusions:

• Cyberspace security issues are deeply intertwined, demanding a holistic approach.
• A significant disparity exists in cybersecurity discourse between professional and non-professional websites.
• Future research should address language limitations and refine rule selection for broader insights.